Optical flow or optic flow is the pattern of apparent motion of objects, surfaces, and edges in a visual scene caused by the relative motion between an observer (an eye or a camera) and the scene. The concept of optical flow was introduced by the American psychologist James J. Gibson in the 1940s to describe the visual stimulus provided to animals moving through the world. Subsequently the term optical flow has been co-opted in the computer vision domain to describe pixel motion in between two temporally ordered images of the scene, and incorporate related techniques of estimation of pixel motion. The optical flow estimation techniques are further integrated in related techniques of image processing and navigation control.
Methods of navigation control are of special interest in advanced driver assistance systems (ADAS). Motion detection in ADAS is fundamental to solving various problems such as object segmentation, time-to-contact information, depth estimation, structure from motion and focus of expansion calculations etc. One of the most widely used techniques for motion detection is the optical flow estimation. Thus, when used in ADAS, accuracy and speed of the optical flow estimation is a critical component in guaranteeing overall reliability of the ADAS. Accordingly, there is a need for a real-time optical flow estimation system having sufficient accuracy in all possible scenarios.
Optical flow estimation is a challenging problem. There are many scenarios where the flow estimation can go wrong such as occlusions, a new object coming into the camera's field of view, or an object going out of the camera's field of view. Camera rotations can make it difficult (or sometimes impossible) to estimate the correct optical flow. Low visibility (night vision, rain or fog), noise in the image, high rates of motion etc., are some other difficult scenarios for optical flow estimation. Flow estimation can also be challenging around moving object boundaries.
The estimated optical flow algorithm is typically used by a higher level algorithm such as the object tracking algorithm. It is important for the higher level algorithm to know where the optical flow estimation is correct and where it is likely to be wrong—so that the higher level algorithm can either discard the potentially incorrect flow estimates or assign proper weight based on the estimated correctness of the optical flows. Accordingly there is a need for computing a confidence map associated with the optical flow. This confidence map then indicates the confidence in the optical flow estimation—whether the flow estimation is likely to be correct or not.